Extracting Quantal Properties of Transmission at Central Synapses

  • Frederic Lanore
  • R. Angus SilverEmail author
Part of the Neuromethods book series (NM, volume 113)


Chemical synapses enable neurons to communicate rapidly, process and filter signals and to store information. However, studying their functional properties is difficult because synaptic connections typically consist of multiple synaptic contacts that release vesicles stochastically and exhibit time-dependent behavior. Moreover, most central synapses are small and inaccessible to direct measurements. Estimation of synaptic properties from postsynaptic currents or potentials is complicated by the presence of nonuniform release probability and nonuniform quantal properties. The presence of multivesicular release and postsynaptic receptor saturation at some synapses can also complicate the interpretation of quantal parameters. Multiple-probability fluctuation analysis (MPFA; also known as variance-mean analysis) is a method that has been developed for estimating synaptic parameters from the variance and mean amplitude of synaptic responses recorded at different release probabilities. This statistical approach, which incorporates nonuniform synaptic properties, has become widely used for studying synaptic transmission. In this chapter, we describe the statistical models used to extract quantal parameters and discuss their interpretation when applying MPFA.

Key words

Synapse Vesicle Active zone Release probability Release site Quantal analysis MPFA Variance-mean analysis 



We thank Antoine Valera for comments on the manuscript. FL is supported by an IEF Marie Curie fellowship (FP7) and RAS holds a Wellcome Trust Principal Research Fellowship and an ERC Advanced Grant.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Neuroscience, Physiology and PharmacologyUniversity College LondonLondonUK

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